Insight into OTFT Sensors Using Confocal Fluorescence Microscopy
Bibliographic record
Abstract
Confocal fluorescence microscopy provides a means to map charge carrier density within the semiconductor layer in an active organic thin film transistor (OTFT). This method exploits the inverse relationship between charge carrier density and photoluminescence (PL) intensity in OTFTs, originating from exciton quenching following exciton-charge energy transfer. This work demonstrates that confocal microscopy can be a simple yet effective approach to gain insight into doping and de-doping processes in OTFT sensors. Specifically, the mechanisms of hydrogen peroxide sensitivity are studied in low-voltage hygroscopic insulator field effect transistors (HIFETs). While the sensitivity of HIFETs to hydrogen peroxide is well known, the underlying mechanisms remain poorly understood. Using confocal microscopy, new light is shed on these mechanisms. Two distinct doping processes are discerned: one that occurs throughout the semiconductor film, independent of applied voltages; and a stronger doping effect occurring near the source electrode, when acting as an anode with respect to a negatively polarized drain electrode. These insights offer important guidance to future studies and the optimization of HIFET-based sensors. More importantly, the methods reported here are broadly applicable to the study of a range of OTFT-based sensors. This work demonstrates that confocal microscopy can be an effective research tool in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".